Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder - Kim, Jaehyun; Shin, Hyunjung (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, 2023)
Data used for the diagnosis of thyroid cancer patients include Thyroid Stimulating hormone (TSH), Thyroglobulin (Tg), Tg Antibodies (TgAb), and pathology information. However, existing thyroid cancer-related studies using machine learning tended to use a single type of data in model. Therefore, this study proposes a new model and method that can use all type of data together. The proposed method uses an autoencoder model composed of multiple encoders to extract comprehensive features from three types of hormone related data and pathology data and connects them to a classification layer to train them to be classified. As a result of the experiment, the proposed model using both types of hormonal and pathological data showed a performance improvement of up to about 58.1 times compared to the model using only a single type of data.
Keywords: Artificial intelligence in medicine; Auto encoders; Cancer patients; Data embedding; Features extraction; Multi-modal; Multi-modality; Recurrence probability; Thyroid cancers; Thyroid stimulating hormones; Artificial Intelligence in Medicine; data embedding; feature extraction; multimodality; prediction; thyroid cancer